Go west: urbanism, mobility, and ingenuity in western Canadian writing and everyday practice
Bibliographic record
Abstract
In early criticism of Western Canadian literature, prairie spaces were constructed as predominantly rural in order to set the region and prairie writing apart from the rest of Canada and other Canadian literature. In time, prairie criticism’s focus on rural realist texts led to the marginalization of urban prairie writing and the construction of urban spaces as corrupt and artificial in comparison to the natural and virtuous rural environment. I work to remedy the absence of urban texts in the criticism of prairie literature, and I argue that prairie cities are dynamic and mobile worlds where prairie inhabitants exercise their agency through everyday practices. Utilizing the work of Raymond Williams, I show how urban and rural spaces are constructed in the canonical prairie texts of Grove, Ostenso, and Stead to serve various capitalist interests and colonial ideologies. I explore the depiction of Winnipeg in Durkin’s The Magpie as a dynamic, complex, and politically engaged space. Moreover, I use Michel de Certeau’s work to assert that the underprivileged and colonized individuals in the city subvert and utilize the systems and organizations of those in power. They develop an increased deviousness and take advantage of incidental and multifarious opportunities that come their way as they work, dwell, and move about in everyday life. Subsequently, I look at urban writing by women, Eastern-European immigrants, and Aboriginal writers and show that they use urban spaces, everyday practices, and writing to exercise their agency. To destabilize unitary forces in language, to depict their own experiences, and to convey their own meanings of home, labour, and community, marginalized writers employ wordplay, humour, historical and cultural references, and intertextuality. I also use Jane M. Jacobs’ work on postcolonial cities and Tim Cresswell’s theories of mobility. I read prairie cities as places of competing mobilities and networks of dominances and resistances, where colonized individuals negotiate complex, hybrid, and authentic identities. The urban prairie texts I explore demonstrate the possibility of political, social, and economic changes, and a beneficial relationship with the prairie environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.047 | 0.039 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".